Reaching for the Cap and Gown: Progress Toward Success Boston's College Completion Goals for Graduates of the Boston Public Schools

A new report, prepared for Mayor Martin J. Walsh and the Success Boston college completion initiative, shows a remarkable increase in both the percentage and the number of Boston Public Schools graduates who complete college within six years. The report also examines college completion for students with Success Boston coaches, a major intervention launched by the Boston Foundation and its partners, including the Boston Public Schools, in 2009. Success Boston, a citywide multi-sector college completion initiative, was launched in 2008 in response to a report that found that only 35% of the BPS Class of 2000 graduates who enrolled in college earned a degree within seven years of graduating high school. The initiative is guided by the Boston Public Schools, the Boston Foundation, UMass Boston, Bunker Hill Community College, and the Boston Private Industry Council, along with dozens of colleges, universities, and nonprofit organizations. Among the initiative's ambitious goals was pushing members of the BPS Class of 2009 to a 52%six-year college completion rate. Today's report, "Reaching for the Cap and Gown: Progress Toward Success Boston's College Completion Goals for Graduates of the Boston Public Schools," finds that the six-year college completion rate of first-year college enrollees from the BPS Class of 2009 was 51.3%--within one percentage point of the 52% goal set in 2008. Equally impressive is the gain in the number of BPS graduates completing college within six years of high school graduation--1,314 from the Class of 2009, compared to 735 from the Class of 2000, the equivalent of a 79% increase. The study also finds that college completion, at 54.7%, is even higher than the goal for students who enrolled in the fall immediately after graduating from high school

A review of Multi-Agent Simulation Models in Agriculture

Multi-Agent Simulation (MAS) models are intended to capture emergent properties of complex systems that are not amenable to equilibrium analysis. They are beginning to see some use for analysing agricultural systems. The paper reports on work in progress to create a MAS for specific sectors in New Zealand agriculture. One part of the paper focuses on options for modelling land and other resources such as water, labour and capital in this model, as well as markets for exchanging resources and commodities. A second part considers options for modelling agent heterogeneity, especially risk preferences of farmers, and the impacts on decision-making. The final section outlines the MAS that the authors will be constructing over the next few years and the types of research questions that the model will help investigate.multi-agent simulation models, modelling, agent-based model, cellular automata, decision-making, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Long term cost effectiveness of interventions for obesity:A Mendelian randomisation study

Background The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches. Methods and findings We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders. Conclusions Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI

Development of a transparent interactive decision interrogator to facilitate the decision-making process in health care.

BACKGROUND: Decisions about the use of new technologies in health care are often based on complex economic models. Decision makers frequently make informal judgments about evidence, uncertainty, and the assumptions that underpin these models. OBJECTIVES: Transparent interactive decision interrogator (TIDI) facilitates more formal critique of decision models by decision makers such as members of appraisal committees of the National Institute for Health and Clinical Excellence in the UK. By allowing them to run advanced statistical models under different scenarios in real time, TIDI can make the decision process more efficient and transparent, while avoiding limitations on pre-prepared analysis. METHODS: TIDI, programmed in Visual Basic for applications within Excel, provides an interface for controlling all components of a decision model developed in the appropriate software (e.g., meta-analysis in WinBUGS and the decision model in R) by linking software packages using RExcel and R2WinBUGS. TIDI's graphical controls allow the user to modify assumptions and to run the decision model, and results are returned to an Excel spreadsheet. A tool displaying tornado plots helps to evaluate the influence of individual parameters on the model outcomes, and an interactive meta-analysis module allows the user to select any combination of available studies, explore the impact of bias adjustment, and view results using forest plots. We demonstrate TIDI using an example of a decision model in antenatal care. CONCLUSION: Use of TIDI during the NICE appraisal of tumor necrosis factor-alpha inhibitors (in psoriatic arthritis) successfully demonstrated its ability to facilitate critiques of the decision models by decision makers

The effects of graded motor imagery and its components on chronic pain: A systematic review and meta-analysis

This is the post-print version of the final paper published in The Journal of Pain. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 The American Pain Society.Graded motor imagery (GMI) is becoming increasingly used in the treatment of chronic pain conditions. The objective of this systematic review was to synthesize all evidence concerning the effects of GMI and its constituent components on chronic pain. Systematic searches were conducted in 10 electronic databases. All randomized controlled trials (RCTs) of GMI, left/right judgment training, motor imagery, and mirror therapy used as a treatment for chronic pain were included. Methodological quality was assessed using the Cochrane risk of bias tool. Six RCTs met our inclusion criteria, and the methodological quality was generally low. No effect was seen for left/right judgment training, and conflicting results were found for motor imagery used as stand-alone techniques, but positive effects were observed for both mirror therapy and GMI. A meta-analysis of GMI versus usual physiotherapy care favored GMI in reducing pain (2 studies, n = 63; effect size, 1.06 [95% confidence interval, .41, 1.71]; heterogeneity, I2 = 15%). Our results suggest that GMI and mirror therapy alone may be effective, although this conclusion is based on limited evidence. Further rigorous studies are needed to investigate the effects of GMI and its components on a wider chronic pain population.NHMR

Engaging communities in addressing air quality: a scoping review

Abstract Background Exposure to air pollution has a detrimental effect on health and disproportionately affects people living in socio-economically disadvantaged areas. Engaging with communities to identify concerns and solutions could support organisations responsible for air quality control, improve environmental decision-making, and widen understanding of air quality issues associated with health. This scoping review aimed to provide an overview of approaches used to engage communities in addressing air quality and identify the outcomes that have been achieved. Methods Searches for studies that described community engagement in air quality activities were conducted across five databases (Academic Search Complete, CABI, GreenFILE, MEDLINE, Web of Science). Data on study characteristics, community engagement approach, and relevant outcomes were extracted. The review process was informed by a multi-stakeholder group with an interest in and experience of community engagement in air quality. Thirty-nine papers from thirty studies were included in the final synthesis. Conclusion A range of approaches have been used to engage communities in addressing air quality, most notably air quality monitoring. Positive outcomes included increased awareness, capacity building, and changes to organisational policy and practice. Longer-term projects and further exploration of the impact of community engagement on improving air quality and health are needed as reporting on these outcomes was limited. </jats:sec

Building from patient experiences to deliver patient-focused healthcare systems in collaboration with patients: A call to action

Patients’ experiences of their diagnosis, condition, and treatment (including the impact on their lives), and their experiences surrounding expectations of care, are becoming increasingly important in shaping healthcare systems that meet the evolving needs and priorities of different patient communities over time; this is an ongoing goal of all healthcare stakeholders. Current approaches that capture patient experiences with data are fragmented, resulting in duplication of effort, numerous requests for information, and increased patient burden. Application of patient experience data to inform healthcare decisions is still emerging and there remains an opportunity to align diverse stakeholders on the value of these data to strengthen healthcare systems. Given the collective value of understanding patient experiences across multiple stakeholder groups, we propose a more aligned approach to the collection of patient experience data. This approach is built on the principle that the patients’ experiences are the starting point, and not just something to be considered at the end of the process. It must also be based on meaningful patient engagement, where patients are collaborators and decision makers at each step, thereby ensuring their needs and priorities are accurately reflected. The resulting data and evidence should be made available for all stakeholders, to inform their decision making and healthcare strategies in ways that meet patient priorities. We call for multi-stakeholder collaboration that will deliver healthcare systems and interventions that are better centered around and tailored to patient experiences, and that will help address patients’ unmet needs

Differential cellular and humoral immune responses in immunocompromised individuals following multiple SARS-CoV-2 vaccinations

Introduction: The heterogeneity of the immunocompromised population means some individuals may exhibit variable, weak or reduced vaccine-induced immune responses, leaving them poorly protected from COVID-19 disease despite receiving multiple SARS-CoV-2 vaccinations. There is conflicting data on the immunogenicity elicited by multiple vaccinations in immunocompromised groups. The aim of this study was to measure both humoral and cellular vaccine-induced immunity in several immunocompromised cohorts and to compare them to immunocompetent controls. Methods: Cytokine release in peptide-stimulated whole blood, and neutralising antibody and baseline SARS-CoV-2 spike-specific IgG levels in plasma were measured in rheumatology patients (n=29), renal transplant recipients (n=46), people living with HIV (PLWH) (n=27) and immunocompetent participants (n=64) post third or fourth vaccination from just one blood sample. Cytokines were measured by ELISA and multiplex array. Neutralising antibody levels in plasma were determined by a 50% neutralising antibody titre assay and SARS-CoV-2 spike specific IgG levels were quantified by ELISA. Results: In infection negative donors, IFN-γ, IL-2 and neutralising antibody levels were significantly reduced in rheumatology patients (p=0.0014, p=0.0415, p=0.0319, respectively) and renal transplant recipients (p<0.0001, p=0.0005, p<0.0001, respectively) compared to immunocompetent controls, with IgG antibody responses similarly affected. Conversely, cellular and humoral immune responses were not impaired in PLWH, or between individuals from all groups with previous SARS-CoV-2 infections. Discussion: These results suggest that specific subgroups within immunocompromised cohorts could benefit from distinct, personalised immunisation or treatment strategies. Identification of vaccine non-responders could be critical to protect those most at risk

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare